A global ionospheric F2 region peak electron density model using neural networks and extended geophysically relevant inputs

by Oyeyemi, Elijah Oyedola

This thesis presents my research on the development of a neural network (NN) based global empirical model of the ionospheric F2 region peak electron density using extended geophysically relevant inputs. The main principle behind this approach has been to utilize parameters other than simple geographic co-ordinates, on which the F2 peak electron density is known to depend, and to exploit the technique of NNs, thereby establishing and modeling the non-linear dynamic processes (both in space and time)associated with the F2 region electron density on a global scale. Four different models have been developed in this work. These are the foF2 NN model, M(3000)F2 NN model, short-term forecasting foF2 NN, and a near-real time foF2 NN model. Data used in the training of the NNs were obtained from the worldwide ionosonde stations spanning the period 1964 to 1986 based on availability, which included all periods of calm and disturbed magnetic activity.

Common input parameters used in the training of all 4 models are day number (day of the year, DN), Universal Time (UT), a 2 month running mean of the sunspot number (R2), a 2 day running mean of the 3-hour planetary magnetic index ap (A16), solar zenith angle (CHI), geographic latitude (q), magnetic dip angle (I), angle of magnetic declination (D), angle of meridian relative to subsolar point (M).

For the short-term and near-real time foF2 models, additional input parameters related to recent past observations of foF2 itself were included in the training of the NNs.

The results of the foF2 NN model and M(3000)F2 NN model presented in this work, which compare favourably with the IRI (International Reference Ionosphere) model successfully demonstrate the potential of NNs for spatial and temporal modeling of the ionospheric parameters foF2 and M(3000)F2 globally. The results obtained from the short-term foF2 NN model and nearreal time foF2 NN model reveal that, in addition to the temporal and spatial input variables, short-term forecasting of foF2 is much improved by including past observations of foF2 itself. Results obtained from the near-real time foF2 NN model also reveal that there exists a correlation between measured foF2 values at different locations across the globe. Again, comparisons of the foF2 NN model and M(3000)F2 NN model predictions with that of the IRI model predictions and observed values at some selected high latitude stations, suggest that the NN technique can successfully be employed to model the complex irregularities associated with the high latitude regions. Based on the results obtained in this research and the comparison made with the IRI model (URSI and CCIR coefficients), these results justify consideration of the NN technique for the prediction of global ionospheric parameters. I believe that, after consideration by the IRI community, these models will prove to be valuable to both the high frequency (HF) communication and worldwide ionospheric communities.